Table of Contents

Class AdaptiveParametersHelper<T, TInput, TOutput>

Namespace
AiDotNet.Helpers
Assembly
AiDotNet.dll

Helper class that provides methods for dynamically adjusting genetic algorithm parameters during optimization.

public static class AdaptiveParametersHelper<T, TInput, TOutput>

Type Parameters

T

The numeric type used for calculations (e.g., double, float).

TInput
TOutput
Inheritance
AdaptiveParametersHelper<T, TInput, TOutput>
Inherited Members

Remarks

For Beginners: This helper class contains methods that automatically adjust the settings of a genetic algorithm while it's running to help it find better solutions.

A genetic algorithm is an AI technique inspired by natural evolution - it creates a "population" of possible solutions, selects the best ones, and combines them to create new solutions, similar to how animals evolve through natural selection.

Two important settings in genetic algorithms are:

  • Crossover rate: How often solutions are combined to create new ones (like breeding)
  • Mutation rate: How often random changes are introduced to solutions (like genetic mutations)

This class helps the algorithm perform better by automatically adjusting these rates based on whether the algorithm is making progress or getting stuck.

Methods

UpdateAdaptiveGeneticParameters(ref T, ref T, OptimizationStepData<T, TInput, TOutput>, OptimizationStepData<T, TInput, TOutput>, GeneticAlgorithmOptimizerOptions<T, TInput, TOutput>)

Updates the crossover and mutation rates based on whether the optimization is improving.

public static void UpdateAdaptiveGeneticParameters(ref T currentCrossoverRate, ref T currentMutationRate, OptimizationStepData<T, TInput, TOutput> currentStepData, OptimizationStepData<T, TInput, TOutput> previousStepData, GeneticAlgorithmOptimizerOptions<T, TInput, TOutput> options)

Parameters

currentCrossoverRate T

The current crossover rate to be updated.

currentMutationRate T

The current mutation rate to be updated.

currentStepData OptimizationStepData<T, TInput, TOutput>

Data from the current optimization step.

previousStepData OptimizationStepData<T, TInput, TOutput>

Data from the previous optimization step.

options GeneticAlgorithmOptimizerOptions<T, TInput, TOutput>

Configuration options for the genetic algorithm optimizer.

Remarks

For Beginners: This method is like an automatic tuning system for the genetic algorithm.

Imagine you're trying to find the best recipe for a cake. If your latest attempt tastes better than the previous one, you might want to:

  • Make more small variations of your successful recipe (increase crossover)
  • Make fewer random changes (decrease mutation)

But if your latest cake tastes worse, you might want to:

  • Try more different approaches (decrease crossover)
  • Make more random changes to escape the bad recipe (increase mutation)

That's exactly what this method does for the genetic algorithm:

  1. It compares the current result (fitness score) with the previous one
  2. If things are improving, it adjusts the settings to focus more on refining good solutions
  3. If things are getting worse, it adjusts the settings to explore more diverse solutions
  4. It always makes sure the settings stay within reasonable limits

This adaptive approach helps the algorithm work better across different problems without requiring manual tuning.

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